The Geoinformation and Big Data Research Lab have unveiled an innovative tool, GIS Copilot, which integrates Large Language Models (LLMs) into Geographic Information Systems (GIS) to enable users to perform spatial analysis using natural language. The GIS Copilot is a step closer to achieving the broader vision of Autonomous GIS, which aim at democratizing access to spatial analysis, making it accessible to users of all expertise levels.
GIS Copilot operates as a plugin for the QGIS platform, enabling users to perform spatial operations through simple natural language, spanning from basic operations such as
- Can you please create 2000-feet zones around each health facilities in Washington DC to identify areas of service coverage?
- Generate contour lines from the DEM of Puerto Rico with a 50-meter interval.
to more complex questions such as
- Generate an obesity risk behavior index of each county in the contiguous US by analyzing the rate of visits to unhealthy food retailers (such as convenience store, alcoholic drinking places, and limited service restaurant) and the visit rate to places that support physical activity (e.g., sports centers, parks, fitness centers). Visualize the results in a thematic map to highlight the obesity risk behavior index across counties.
- Could you analyze and visualize the fast food accessibility score for each county based on the number of fast food restaurants and population using a thematic map with blue graduated colors. Then, analyze the correlation between the county-level obesity rate and the fast food accessibility score by drawing a scatter plot with a regression line.
to the development of interactive web mapping applications such as
- Generate an interactive web map using leaflet for the shown data layer.
The tool’s functionality was rigorously evaluated on more than 100 spatial analysis tasks, categorized into three levels of complexity including Basic Tasks: Single-step operations involving one GIS tool and data layer. Intermediate Tasks: Multi-step processes requiring multiple tools and user guidance. Advanced Tasks: Complex, multi-step analyses where the tool independently determines and executes workflows without explicit user input. Results showed that GIS Copilot excels at automating basic and intermediate tasks, with significant progress in handling advanced workflows. While challenges remain in achieving complete autonomy for highly complex tasks, the tool represents a major step toward the vision of autonomous GIS.
The release of GIS Copilot has sparked massive interest in the geospatial and AI communities. A LinkedIn post announcing the tool has garnered over 150,000 impressions, 2,200 likes, and more than 220 reposts within just a few days. The overwhelming response reflects the demand for such an GIS Copilot that simplify GIS workflows and enhance accessibility.
GIS Copilot’s source code is available on GitHub, with the plugin downloadable from the official QGIS plugin page. The research team has also made data and case studies used in testing accessible online, inviting collaboration and feedback from the global GIS community.
The GIS Copilot represents a significant milestone in the development of Autonomou GIS by integrating AI with GIS, bridging the gap between technical GIS expertise and practical application. This innovation not only simplifies geospatial workflows but also enhances decision-making across diverse domains such as disaster management, urban planning, and public health.
To learn more about GIS Copilot’s design, implementation, and discussions, please check out our preprint paper.
